M365 Show Podcast

Mirko Peters

Welcome to the M365 Show — your essential podcast for everything Microsoft 365, Azure, and beyond. Join us as we explore the latest developments across Power BI, Power Platform, Microsoft Teams, Viva, Fabric, Purview, Security, and the entire Microsoft ecosystem. Each episode delivers expert insights, real-world use cases, best practices, and interviews with industry leaders to help you stay ahead in the fast-moving world of cloud, collaboration, and data innovation. Whether you're an IT professional, business leader, developer, or data enthusiast, the M365 Show brings the knowledge, trends, and strategies you need to thrive in the modern digital workplace. Tune in, level up, and make the most of everything Microsoft has to offer. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.

  1. 5 GIỜ TRƯỚC

    The Autonomous Agent Excel Hack

    🔍 Key Topics Covered 1) The Anatomy of an Autonomous Agent (Blueprint) What “autonomous” means in Copilot Studio: Trigger → Logic → Orchestration.Division of labor: Power Automate (email trigger, SharePoint staging, outbound reply) + Copilot Studio Agent (read Excel table, generate answers, write back).End-to-end path: Email → SharePoint → Copilot Studio → Power Automate → Reply.Why RFIs are perfect: predictable schema (Question/Answer), high repetition, low tolerance for errors.2) Feeding the Machine — Input Flow Design (Power Automate) Trigger: New email in shared mailbox; filter .xlsx only (ditch PDFs/screenshots).Structure check: enforce a named table (e.g., Table1) with columns like Question/Answer.Staging: copy to SharePoint for versioning, stable IDs, and compliance.Pass File ID + Message ID to the agent with a clear, structured prompt (scope, action, destination).3) The AI Brain — Generative Answer Loop (Copilot Studio) Topic takes File ID, runs List Rows in Table, iterates rows deterministically.One question at a time to prevent context bleed; disable “send message” and store outputs in a variable.Generate answer → Update matching row in the same Excel table via SharePoint path.Knowledge grounding options:Internal (SharePoint/Dataverse) for precision & compliance.Web (Bing grounding) for general info—use cautiously in regulated contexts.Result: a clean read → reason → respond → record loop.4) The Write-Back & Reply Mechanism (Power Automate) Timing guardrails: brief delay to ensure SharePoint commits changes (sync tolerance).Get File Content (binary) → Send email reply with the updated workbook attached, preserve thread via Message ID.Resilience: table-not-found → graceful error email; consider batching/parallelism for large sheets.5) Scaling, Governance, and Reality Checks Quotas & throttling exist—design for bounded autonomy and least privilege.When volume grows: migrate from raw Excel to Dataverse/SharePoint lists for concurrency and reliability.Telemetry & audits: monitor flow runs, agent transcripts, and export logs; adopt DLP, RBAC, change control.Human-in-the-loop QA for sampled outputs; combine automated checks with manual review.Future-proofing: this pattern extends to multi-agent orchestration (specialized bots collaborating).🧠 Key Takeaways Automation ≠ typing faster. It’s removing typing entirely.Use Power Automate to detect, validate, stage, and dispatch; use Copilot Studio to read, reason, and write back.Enforce named tables and clean schemas—merged cells are the enemy.Prefer internal knowledge grounding for reliable, compliant answers.Design for governance from day one: least privilege, logs, and graceful failure paths.✅ Implementation Checklist (Copy/Paste Ready) Shared mailbox created; Power Automate trigger: New email (with attachments).Filter .xlsx; reject non-Excel files with a friendly notice.Enforce named table (Table1) with Question/Answer columns.Copy to SharePoint library; capture File ID + Message ID.Call Copilot Studio Agent with structured parameters (file scope, action, reply target).In Copilot: List rows → per-row Generate Answer (internal grounding) → Update row.Back in Power Automate: Delay 60–120s, Get File Content, Reply with attachment (threaded).Error paths: missing table/columns → notify sender; log run IDs.Monitoring: flow history, agent transcripts, log exports to Log Analytics/Sentinel.Pilot on a small RFI set; then consider Dataverse for scale.🎧 Listen & Subscribe If this frees you from another week of copy-paste purgatory, follow the show and turn on notifications. Next up: evolving this pattern from Excel into Dataverse-first multi-agent workflows—because true autonomy comes with proper data design. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support. Follow us on: LInkedIn Substack

    23 phút
  2. 17 GIỜ TRƯỚC

    M365 Show - Microsoft 365 Digital Workplace Daily - The Secret to Putting SQL Data in Copilot Studio

    🔍 Key Topics Covered 1) Why Copilots Fail Without ContextLLMs without data grounding = fluent hallucinations and confident nonsense.The real memory lives in SQL Server—orders, invoices, inventory—behind the firewall.Hybrid parity goal: cloud intelligence with on-prem control, zero data exposure.2) The Power Platform Data Gateway — Spine of Hybrid AINot “middleware”—your encrypted, outbound-only tunnel (no inbound firewall punches).Gateway clusters for high availability; one gateway serves Power BI, Power Apps, Power Automate, and Copilot Studio.No replication: queries only, end-to-end TLS, AAD/SQL/Windows auth, and auditable telemetry.3) Teaching Copilot to Read SQL (Knowledge Sources)Add Azure SQL via Gateway in Copilot Studio; choose the right auth (SQL, Windows, or AAD-brokered).Expose clean views (well-named columns, read-optimized joins) for clarity and performance.Live answers: conversational context drives real-time T-SQL through the gateway—no CSV exports.4) Giving Copilot Hands — Actions & Write-BacksDefine SQL Actions (insert/update/execute stored procs) with strict parameter prompts.Separate read vs write connections/privileges for least privilege; confirmations for critical ops.Every write is encrypted, logged, and governed—from chat intent to committed row.5) Designing the Hybrid Brain — Architecture & ScaleFour-part model: SQL (memory) → Gateway (spine) → Copilot/Power Platform (brain) → Teams/Web (face).Scale with gateway clusters, indexes, read-optimized views, and nightly metadata refresh.Send logs to Log Analytics/Sentinel; prove compliance with user/time/action traces.🧠 Key TakeawaysCopilot without SQL context = eloquent guesswork. Ground it via the Data Gateway.The gateway is outbound-only, encrypted, auditable—no database exposure.Use Knowledge Sources for live reads and SQL Actions for safe, governed writes.Design for least privilege, versioned views, and telemetry from day one.Hybrid done right = real-time answers + compliant operations.✅ Implementation Checklist (Practical)Install & register On-Premises Data Gateway; create a cluster (2+ nodes).Create environment connections: separate read (SELECT) and write (INSERT/UPDATE) creds.In Copilot Studio: Add Knowledge → Azure SQL via gateway → select read-optimized views.Verify live queries (small, filtered result sets; correct data types).Define SQL Actions with clear parameter labels & confirmations.Enable telemetry export to Log Analytics/Sentinel; document runbooks.Index & maintain views; schedule metadata refresh.Pen test: cert chain, outbound rules, least privilege review.Pilot with a narrow use case (e.g., “invoice lookup + create customer”).Roll out with RBAC, DLP policies, and change control.🎧 Listen & Subscribe If this saved you from another late-night CSV shuffle, follow the show and turn on notifications. Next up: extending the same architecture to legacy APIs and flat-file systems—because proper wiring beats magic every time. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support. Follow us on: LInkedIn Substack

    21 phút
  3. 1 NGÀY TRƯỚC

    The Custom Connector Lie: How to Really Add MCP to Copilot Studio

    🔍 Key Topics Covered 1) The Illusion of Simplicity Why the “Add Tool → Model Context Protocol” UI only surfaces built-ins (Dataverse/SharePoint/etc.).The difference between “appears in the list” and actually exchanging streamable context.Why your “connected MCP” is often a placebo until you build the bridge.2) What MCP Actually Is (and Isn’t) MCP as a lingua franca for agents and context sources—tools, actions, schemas, parameters, tokens.Streaming-first behavior: partial, evented payloads for live reasoning (not bulk dumps).Protocol ≠ data source: MCP standardizes the handshake and structure so AI can reason with governed context.3) Building a Real Custom Connector (The Unvarnished Path) Where to start: create connector in Power Apps Make, not inside Copilot Studio.Template choice matters (streamable variant) and why “no-auth” is common in tenant-isolated setups.The two silent killers:Host must be the bare domain (no https://, no /api/mcp).Base URL must not duplicate route prefixes (avoid /api/mcp/api/mcp).Schema alignment to MCP spec: exact casing, array vs object types, required fields.Enable streaming (chunked transfer) or expect truncation/timeouts.Certificates & proxies: trust chains, CDNs that strip streaming headers, and why “optimizations” break MCP.Naming & caching quirks: unique names, patient publication, and avoiding “refresh-loop purgatory.”4) Testing & Verification That Actually Proves It Works Visibility test: does your MCP tool appear in Copilot Studio after propagation?Metadata handshake: do tool descriptions & parameters arrive from your server?Functional probes: ask controlled queries and watch for markdown + citations arriving as a stream.Failure decoding:Empty responses → URL path misalignment.Truncated markdown → missing chunked transfer.“I don’t know how to help” → schema mismatch.Connection flaps → SSL/CA chain or proxy stripping.Network sanity checks: confirm data: event chunks vs single payload dumps.5) Why This Matters Beyond the Demo Governance & auditability: sanctioned sources, explicit logs, repeatable citations.Security posture: least-privilege connectors as embassy checkpoints (not open tunnels).Zero-hallucination culture: MCP narrows the AI to approved truth.Future-proofing: aligning to inter-agent standards as enterprise prerequisites.🧠 Key Takeaways MCP ≠ data feed. It’s a protocol for structured, streamable context exchange.Custom connectors ≠ shortcuts. They’re protocol translators you must design with schema + streaming discipline.The MCP dropdown lists native servers; your custom MCP needs a real bridge to appear and function.Testing is a protocol rehearsal—check visibility, metadata, streaming, and citations before you claim success.Done right, MCP transforms Copilot from chatbot to compliant analyst with traceable sources.✅ Implementation Checklist (Practical & Brutally Honest) Create connector in Power Apps Make (solution-aware).Choose streamable MCP template; leave auth minimal unless policy requires more.Host = bare domain only; Base URL = correct, no duplicate prefixes.Align request/response schemas to MCP spec (casing, shapes, required fields).Enable streaming; verify Transfer-Encoding: chunked.Use valid TLS; avoid proxies that strip streaming headers.Publish and wait (don’t refresh-loop).In Copilot Studio: add tool, confirm metadata import.Run controlled queries; confirm incremental render + citations.Log & monitor: document failures, headers, and schema diffs for reuse.🎧 Listen & Subscribe If this episode saved you from another “connected but silent” demo, follow the show and turn on notifications. Future episodes land like a compliant connector: once, on time, fully streamed, with citations. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support. Follow us on: LInkedIn Substack

    24 phút
  4. 1 NGÀY TRƯỚC

    STOP Building Cloud Flows! Use Agent Flows Instead

    🔍 Key Topics Covered 1) The Hidden Price Tag of Cloud Flows Why “build an Automated Cloud Flow” often means “start a licensing tab.”Premium connector ripple effect: add Dataverse/SQL/Salesforce and everyone touching the flow may need premium.API call quotas & throttling: the invisible brake on your “set it and forget it” automations.AI Builder double-pay: automation fees here, AI credits there—two currencies, one outcome: sprawl.2) Enter Agent Flows — Automation with a Copilot Brain Lives in Copilot Studio; billed by messages/actions, not by who uses it.Premium & custom connectors included under consumption.AI capabilities (classification, extraction, summarization) aligned to the same credit pool.Triggers from conversation, intent, or signals—automation that interprets before it executes.3) When Agent Flows Replace Cloud Flows (and When They Don’t) Use Agent Flows for chat/intent-driven, personal, or AI-assisted tasks where usage is bursty and user-specific.Keep Cloud Flows for shared, scheduled, multi-owner orchestration across teams.Migration path: make the Cloud Flow solution-aware → switch plan to Copilot Studio → it becomes an Agent Flow (one-way).Governance parity: drafts, versions, audit logs, RBAC—now inside Copilot Studio.4) The Math: Why Consumption Wins Cloud Flows = “buffet priced per person.” Great if maxed; wasteful if idle.Agent Flows = “à la carte per action.” Costs scale linearly with actual work.Transparent cost tracing by flow, connector, and hour; predictable quotas; no surprise overages.Optimization matters: consolidate actions, reduce chat hops, and you literally pay less.5) Strategy Shift — Automation Goes AI-Native Cloud Flows built the highways; Agent Flows drive themselves along them.Consolidate small, conversational automations into Copilot Studio to reduce double-licensing.Treat every automation as a service inside an intelligent platform, not a one-off per-user asset.Roadmap reality: AI-native orchestration becomes the default entry point; Cloud Flows remain the backend muscle.🧠 Key Takeaways Cloud Flows automate structure; Agent Flows automate intelligence.If it starts in Copilot/chat, is personalized, or spiky in usage—move it to Agent Flows.If it’s shared, scheduled, cross-team infrastructure—Cloud Flows still shine.Message-based billing converts licensing drama into straight arithmetic.Make “solution-aware” your default; design with governance, versioning, and quotas in mind.🎯 Who Should Listen Power Platform makers tired of hitting premium walls.IT leaders/CFOs chasing cost control and clean licensing.Automation architects moving to AI-native orchestration.Ops leaders who want predictable spend and audit-ready governance.🧩 Practical Checklist: Pick the Right Flow Trigger is conversational or AI-driven? → Agent FlowNeeds premium connectors but limited users? → Agent Flow (consumption)Shared, scheduled, cross-department approvals? → Cloud FlowLong-running batch or high-visibility orchestration? → Cloud FlowDesire tight cost tracing & quotas? → Agent Flow in Copilot Studio🎧 Listen & Subscribe If this episode saved your budget—or your weekend—follow the show and turn on notifications. New episodes land like a well-governed quota: predictable, clean, on time. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support. Follow us on: LInkedIn Substack

    20 phút
  5. 2 NGÀY TRƯỚC

    Code Interpreter vs. Azure Functions: Stop The Python Misuse!

    🔍 Key Topics Covered 1️⃣ The Python Problem in Power PlatformWhy “Python runs natively” doesn’t mean “Python runs anywhere.”The rise of Code Interpreter inside Copilot Studio—and the chaos that followed.The real reason flows time out and files hit 512 MB limits.Why using Azure Functions for everything—or nothing—is equally misguided.2️⃣ The Code Interpreter: Microsoft’s New Python SandboxHow Code Interpreter works inside Copilot Studio (the “glass terrarium” analogy).Admin controls: why Python execution is disabled by default.What it can actually do: CSV transformations, data cleanup, basic analytics.Key limitations: no internet calls, no pip installs, and strict timeouts.Why Microsoft made it intentionally safe and limited for business users.Real-world examples of using it correctly for ad-hoc data prep and reporting.3️⃣ Azure Functions: Python Without Training WheelsWhat makes Azure Functions the true enterprise-grade Python runtime.The difference between sandbox snippets and event-driven microservices.How Azure Functions scales automatically, handles dependencies, and logs everything.Integration with Power Automate and Power Apps for secure, versioned automation.Governance, observability, and why IT actually loves this model.Example: processing gigabytes of sales data without breaking a sweat.4️⃣ The Illusion of ConvenienceWhy teams keep mistaking Code Interpreter for production infrastructure.How “sandbox convenience” turns into “production chaos.”The cost illusion: why “free inside Power Platform” still burns your capacity.The hidden governance risks of unmonitored Copilot scripts.How Azure Functions delivers professional reliability vs. chat-prompt volatility.5️⃣ The Decision Framework — When to Use WhichA practical rulebook for choosing the right tool:Code Interpreter = immediate, disposable, interactive.Azure Functions = recurring, scalable, governed.Governance and compliance boundaries between Power Platform and Azure.Security contrasts: sandbox vs. managed identities and VNET isolation.Maintenance and version control differences—why prompts don’t scale.The “Prototype-to-Production Loop”: start ideas in Code Interpreter, deploy in Functions.How to align analysts and architects in one workflow.6️⃣ The Enterprise Reality CheckHow quotas, throttles, and limits affect Python inside Power Platform.Understanding compute capacity and why Code Interpreter isn’t truly “free.”Security posture: sandbox isolation vs. Azure-grade governance.Cost models: prepaid licensing vs. consumption billing.Audit readiness: why Functions produce evidence and prompts produce panic.Real-world governance failure stories—and how to prevent them.7️⃣ Final Takeaway: Stop the MisuseCode Interpreter is for experiments, not enterprise pipelines.Azure Functions is for scalable, auditable, production-ready automation.Mixing them up doesn’t make you clever—it makes you a liability.Prototype fast in Copilot, deploy properly in Azure.Because “responsible architecture” isn’t a buzzword—it’s how you keep your job.🧠 Key TakeawaysCode Interpreter = sandbox: great for small data prep, visualizations, or lightweight automations inside Copilot Studio.Azure Functions = infrastructure: perfect for production workloads, scalable automation, and secure integration across systems.Don’t confuse ease for capability. The sandbox is for testing; the Function is for delivering.Prototype → Promote → Deploy: the golden loop that balances agility with governance.Governance, monitoring, and cost management matter as much as performance.🔗 Episode Mentions & ResourcesMicrosoft Docs: Python in Power Platform (Code Interpreter)Azure Functions OverviewPower Platform Admin Center — Enable Code ExecutionCopilot Studio for Power Platform🎧 Listen & Subscribe If this episode saved you from another flow timeout or a late-night “why did it fail again?” crisis—subscribe wherever you get your podcasts. Follow for upcoming deep dives into:Copilot in the enterpriseAI governance frameworksLow-code meets pro-code: the future of automationHit Follow, enable notifications, and let every new episode arrive like a scheduled task—on time, with zero drama. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support. Follow us on: LInkedIn Substack

    21 phút
  6. 2 NGÀY TRƯỚC

    M365 Show - Microsoft 365 Digital Workplace Daily - Copilot now included with Word, Excel, PowerPoint, Outlook & OneNote

    🔍 Overview Microsoft’s Copilot is now free and fully integrated into the Microsoft 365 ecosystem — Word, Excel, PowerPoint, Outlook, and OneNote. But behind the marketing glow of “AI everywhere,” there’s a deeper truth: Copilot doesn’t add magic; it redistributes intelligence through Microsoft Graph, analyzing your work habits and connected data to make your context visible. In this episode, we break down how Copilot actually works, what it changes in your workflow, and why IT admins, compliance officers, and everyday users all need to pay attention. 🧠 Key Takeaways Copilot isn’t new magic — it’s data orchestration. Microsoft Graph connects your files, emails, and meetings, letting Copilot summarize and respond contextually.Free inclusion ≠ free responsibility. Privacy, compliance, and audit workloads double once Copilot enters your tenant.Every app now speaks to the same AI brain. From Outlook summaries to Excel insights, your work environment has become a shared data ecosystem.✉️ Section 1: Outlook — AI Becomes Your Inbox Butler Copilot transforms Outlook into a triage assistant that: Summarizes long email threadsSuggests polished repliesSurfaces key updates and deadlinesBut here’s the catch: It only sees what you can see (via Microsoft Graph permissions).Poor data-loss-prevention (DLP) setup can lead to accidental leaks.Summaries inherit sensitivity labels, but screenshots remain label-immune.Governance Tip: Enable Purview logging and Copilot activity tracking to trace how AI-generated summaries are shared. Outlook Copilot boosts efficiency—but also raises audit stakes. 📝 Section 2: Word — Drafting With Context-Aware Precision Word Copilot acts like an AI editor who’s read every file you’ve ever saved: Generates executive summaries from your draftsAdapts tone and structure dynamicallyPulls context from OneDrive, Teams notes, and prior versionsBenefits: Rapid editing, style consistency, contextual recall. Risks: Over-sharing sensitive content from linked sources. Governance Recommendations: Turn on Policy Tips for Generated Content to warn users when AI references restricted files.Use audit logs to capture Copilot prompts, outputs, and related file IDs.Used wisely, Word Copilot elevates writing quality; used blindly, it’s a compliance nightmare in polished prose. 📊 Section 3: Excel — The Data Whisperer (or Liability Amplifier) Excel Copilot reads your tables like a seasoned analyst: Generates visualizations from natural-language queriesDetects relationships across datasets automaticallyProvides trend summaries and pivot recommendationsBut contextual power cuts both ways: It may correlate confidential datasets you never meant to link.Inaccurate permissions or mis-labeled data can surface protected information.Best Practice: Apply sensitivity labels to workbooks and enable Copilot policy enforcement before letting it auto-analyze corporate data. ⚙️ Admin & Compliance Essentials To safely deploy Microsoft Copilot: Configure Microsoft Purview DLP policiesAudit Copilot activity eventsDefine acceptable-use guidelines for AI outputsTrain users on label inheritance and sharing boundaries🚨 Final Thoughts Copilot accelerates productivity but also amplifies governance complexity. Your apps may feel smarter — but only because you just became more visible to them. Free Copilot means faster workflows, not freer compliance. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support. Follow us on: LInkedIn Substack

    24 phút
  7. The Security Intern Is Now A Terminator

    3 NGÀY TRƯỚC

    The Security Intern Is Now A Terminator

    Opening: “The Security Intern Is Now A Terminator”Meet your new intern. Doesn’t sleep, doesn’t complain, doesn’t spill coffee into the server rack, and just casually replaced half your Security Operations Center’s workload in a week.This intern isn’t a person, of course. It’s a synthetic analyst—an autonomous agent from Microsoft’s Security Copilot ecosystem—and it never asks for a day off.If you’ve worked in a SOC, you already know the story. Humans drowning in noise. Every endpoint pings, every user sneeze triggers a log—most of it false, all of it demanding review. Meanwhile, every real attack is buried under a landfill of “possible events.”That’s not vigilance. That’s punishment disguised as productivity.Microsoft decided to automate the punishment. Enter Security Copilot agents: miniature digital twins of your best analysts, purpose-built to think in context, make decisions autonomously, and—this is the unnerving part—improve as you correct them.They’re not scripts. They’re coworkers. Coworkers with synthetic patience and the ability to read a thousand alerts per second without blinking.We’re about to meet three of these new hires.Agent One hunts phishing emails—no more analyst marathons through overflowing inboxes.Agent Two handles conditional access chaos—rewriting identity policy before your auditors even notice a gap.Agent Three patches vulnerabilities—quietly prepping deployments while humans argue about severity.Together, they form a kind of robotic operations team: one scanning your messages, one guarding your doors, one applying digital bandages to infected systems.And like any overeager intern, they’re learning frighteningly fast.Humans made them to help. But in teaching them how we secure systems, we also taught them how to think about defense. That’s why, by the end of this video, you’ll see how these agents compress SOC chaos into something manageable—and maybe a little unsettling.The question isn’t whether they’ll lighten your workload. They already have.The question is how long before you report to them.Section 1: The Era of Synthetic AnalystsSecurity Operations Centers didn’t fail because analysts were lazy. They failed because complexity outgrew the species.Every modern enterprise floods its SOC with millions of events daily. Each event demands attention, but only a handful actually matter—and picking out those few is like performing CPR on a haystack hoping one straw coughs.Manual triage worked when logs fit on one monitor. Then came cloud sprawl, hybrid identities, and a tsunami of false positives. Analysts burned out. Response times stretched from hours to days. SOCs became reaction machines—collecting noise faster than they could act.Traditional automation was supposed to fix that. Spoiler: it didn’t.Those old-school scripts are calculators—they follow formulas but never ask why. They trigger the same playbook every time, no matter the context. Useful, yes, but rigid.Agentic AI—what drives Security Copilot’s new era—is different. Think of it like this: the calculator just does math; the intern with intuition decides which math to do.Copilot agents perceive patterns, reason across data, and act autonomously within your policies. They don’t just execute orders—they interpret intent. You give them the goal, and they plan the steps.Why this matters: analysts spend roughly seventy percent of their time proving alerts aren’t threats. That’s seven of every ten work hours verifying ghosts. Security Copilot’s autonomous agents eliminate around ninety percent of that busywork by filtering false alarms before a human ever looks.An agent doesn’t tire after the first hundred alerts. It doesn’t degrade in judgment by hour twelve. It doesn’t miss lunch because it never needed one.And here’s where it gets deviously efficient: feedback loops. You correct the agent once—it remembers forever. No retraining cycles, no repeated briefings. Feed it one “this alert was benign,” and it rewires its reasoning for next time. One human correction scales into permanent institutional memory.Now multiply that memory across Defender, Purview, Entra, and Intune—the entire Microsoft security suite sprouting tiny autonomous specialists.Defender’s agents investigate phishing. Purview’s handle insider risk. Entra’s audit access policies in real time. Intune’s remediate vulnerabilities before they’re on your radar. The architecture is like a nervous system: signals from every limb, reflexes firing instantly, brain centralized in Copilot.The irony? SOCs once hired armies of analysts to handle alert volume; now they deploy agents to supervise those same analysts.Humans went from defining rules, to approving scripts, to mentoring AI interns that no longer need constant guidance.Everything changed at the moment machine reasoning became context-aware. In rule-based automation, context kills the system—too many branches, too much logic maintenance. In agentic AI, context feeds the system—it adapts paths on the fly.And yes, that means the agent learns faster than the average human. Correction number one hundred sticks just as firmly as correction number one. Unlike Steve from night shift, it doesn’t forget by Monday.The result is a SOC that shifts from reaction to anticipation. Humans stop firefighting and start overseeing strategy. Alerts get resolved while you’re still sipping coffee, and investigations run on loop even after your shift ends.The cost? Some pride. Analysts must adapt to supervising intelligence that doesn’t burn out, complain, or misinterpret policies. The benefit? A twenty-four–hour defense grid that gets smarter every time you tell it what it missed.So yes, the security intern evolved. It stopped fetching logs and started demanding datasets.Let’s meet the first one.It doesn’t check your email—it interrogates it.Section 2: Phishing Triage Agent — Killing Alert FatigueEvery SOC has the same morning ritual: open the queue, see hundreds of “suspicious email” alerts, sigh deeply, and start playing cyber roulette. Ninety of those reports will be harmless newsletters or holiday discounts. Five might be genuine phishing attempts. The other five—best case—are your coworkers forwarding memes to the security inbox.Human analysts slog through these one by one, cross-referencing headers, scanning URLs, validating sender reputation. It’s exhausting, repetitive, and utterly unsustainable. The human brain wasn’t designed to digest thousands of nearly identical panic messages per day. Alert fatigue isn’t a metaphor; it’s an occupational hazard.Enter the Phishing Triage Agent. Instead of being passively “sent” reports, this agent interrogates every email as if it were the world’s most meticulous detective. It parses the message, checks linked domains, evaluates sender behavior, and correlates with real‑time threat signals from Defender. Then it decides—on its own—whether the email deserves escalation.Here’s the twist. The agent doesn’t just apply rules; it reasons in context. If a vendor suddenly sends an invoice from an unusual domain, older systems would flag it automatically. Security Copilot’s agent, however, weighs recent correspondence patterns, authentication results, and content tone before concluding. It’s the difference between “seems odd” and “is definitely malicious.”Consider a tiny experiment. A human analyst gets two alerts: “Subject line contains ‘payment pending.’” One email comes from a regular partner; the other from a domain off by one letter. The analyst will investigate both—painstakingly. The agent, meanwhile, handles them simultaneously, runs telemetry checks, spots the domain spoof, closes the safe one, escalates the threat, and drafts its rationale—all before the human finishes reading the first header.This is where natural language feedback changes everything. When an analyst intervenes—typing, “This is harmless”—the agent absorbs that correction. It re‑prioritizes similar alerts automatically next time. The learning isn’t generalized guesswork; it’s specific reasoning tuned to your environment. You’re building collective memory, one dismissal at a time.Transparency matters, of course. No black‑box verdicts. The agent generates a visual workflow showing each reasoning step: DNS lookups, header anomalies, reputation scores, even its decision confidence. Analysts can reenact its thinking like a replay. It’s accountability by design.And the results? Early deployments show up to ninety percent fewer manual investigations for phishing alerts, with mean‑time‑to‑validate dropping from hours to minutes. Analysts spend more time on genuine incidents instead of debating whether “quarterly update.pdf” is planning a heist. Productivity metrics improve not because people work harder, but because they finally stop wasting effort proving the sky isn’t falling.Psychologically, that’s a big deal. Alert fatigue doesn’t just waste time—it corrodes morale. Removing the noise restores focus. Analysts actually feel competent again rather than chronically overwhelmed. The Phishing Triage Agent becomes the calm, sleepless colleague quietly cleaning the inbox chaos before anyone logs in.Basically, this intern reads ten thousand emails a day and never asks for coffee. It doesn’t glance at memes, doesn’t misjudge sarcasm, and doesn’t forward chain letters to the CFO “just in case.” It just works—relentlessly, consistently, boringly well.Behind the sarcasm hides a fundamental shift. Detection isn’t about endless human vigilance anymore; it’s about teaching a machine to approximate your vigilance, refine it, then exceed it. Every correction you make today becomes institutional wisdom tomorrow. Every decision compounds.So your inbox stays clean, your analysts stay sane, and your genuine th

    22 phút
  8. 5 Power Automate Hacks That Unlock Copilot ROI

    3 NGÀY TRƯỚC

    5 Power Automate Hacks That Unlock Copilot ROI

    Opening – Hook + Teaching PromiseYou think Copilot does the work by itself? Fascinating. You deploy an AI assistant and then leave it unsupervised like a toddler near a power socket. And then you complain that it doesn’t deliver ROI. Of course it doesn’t. You handed it a keyboard and no arms.Here’s the inconvenient truth: Copilot saves moments, not money. It can summarize a meeting, draft a reply, or suggest a next step, but those micro‑wins live and die in isolation. Without automation, each one is just a scattered spark—warm for a second, useless at scale. Organizations install AI thinking they bought productivity. What they bought was potential, wrapped in marketing.Now enter Power Automate: the hidden accelerator Microsoft built for people who understand that potential only matters when it’s executed. Copilot talks; Power Automate moves. Together, they create systems where a suggestion instantly becomes an action—documented, auditable, and repeatable. That’s the difference between “it helped me” and “it changed my quarterly numbers.”So here’s what we’ll dissect. Five Power Automate hacks that weaponize Copilot:Custom Connectors—so AI sees past its sandbox.Adaptive Cards—to act instantly where users already are.DLP Enforcement—to keep the brilliant chaos from leaking data.Parallelism—for the scale Copilot predicts but can’t handle alone.And Telemetry Integration—because executives adore metrics more than hypotheses.By the end, you’ll know how to convert chat into measurable automation—governed, scalable, and tracked down to the millisecond. Think of it as teaching your AI intern to actually do the job, ethically and efficiently. Now, let’s start by giving it eyesight.1. Custom Connectors – Giving Copilot Real ContextCopilot’s biggest limitation isn’t intelligence; it’s blindness. It can only automate what it can see. And the out‑of‑box connectors—SharePoint, Outlook, Teams—are a comfortable cage. Useful, predictable, but completely unaware of your ERP, your legacy CRM, or that beautifully ugly database written by an intern in 2012.Without context, Copilot guesses. Ask for a client credit check and it rummages through Excel like a confused raccoon. Enter Custom Connectors—the prosthetic vision you attach to your AI so it stops guessing and starts knowing.Let’s clarify what they are. A Custom Connector is a secure bridge between Power Automate and anything that speaks REST. You describe the endpoints—using an OpenAPI specification or even a Postman collection—and Power Automate treats that external service as if it were native. The elegance is boringly technical: define authentication, map actions, publish into your environment. The impact is enormous: Copilot can now reach data it was forbidden to touch before.The usual workflow looks like this. You document your service endpoints—getClientCreditScore, updateInvoiceStatus, fetchInventoryLevels. Then you define security through Azure Active Directory so every call respects tenant authentication. Once registered, the connector appears inside Power Automate like any of the standard ones. Copilot, working through Copilot Studio or through a prompt in Teams, can now trigger flows using those endpoints. It transforms from a sentence generator into a workflow conductor.Picture this configuration in practice. Copilot receives a prompt in Teams: “Check if Contoso’s account is eligible for extended credit.” Instead of reading a stale spreadsheet, it triggers your flow built on the Custom Connector. That flow queries an internal SQL database, applies your actual business rules, and posts the verified status back into Teams—instantly. No manual lookups, no “hold on while I find that.” The AI didn’t just talk. It acted, with authority.Why it matters is stunningly simple. Every business complains that Copilot can’t access “our real data.” That’s by design—security before functionality. Custom Connectors flip that equation safely. You expose exactly what’s needed—no more, no less—sealed behind tenant-level authentication. Suddenly Copilot’s suggestions are grounded in truth, not hallucination.Here’s the takeaway principle: automation without awareness is randomization. Custom Connectors make aware automation possible.Now, the trap most admins fall into—hardcoding credentials. They create a proof of concept using a personal service account token, then accidentally ship it into production. Congratulations, you just built a time bomb that expires quietly and takes half your flows down at midnight. Always rely on Azure AD OAuth flows or managed identity authentication. Policies first, convenience later.Another overlooked detail: API definitions. Document them properly. Outdated schema or response parameters cause silent failures that look like Copilot indecision but are actually malformed contracts. Validation isn’t optional; it’s governance disguised as sanity.Let’s run through a miniature build to demystify it. Start in Power Automate. Under Data, choose Custom Connectors, then “New from OpenAPI file.” Import your specification. Define authentication as Azure AD and specify resource URLs. Next, run the test operation—if “200 OK” appears, you’ve just taught Power Automate a new vocabulary word. Save, publish, and now that connector becomes available inside flow designer and Copilot Studio.From Copilot’s perspective, it’s now fluent in your internal language. When a user in Copilot Studio crafts a skill like “get customer risk level,” it calls the connector transparently. The AI doesn’t care that data lived behind a firewall; you engineered the tunnel.This is where ROI begins. You’ve eliminated a manual query that might take a financial analyst five minutes each time. Multiply that across hundreds of requests per week, and you’ve translated Copilot’s ideas into measurable time reduction. Automation scales the insight. That’s ROI with receipts.One small refinement: always register these connectors at the environment or solution level, not per user. Otherwise you create a nightmare of duplicated connectors, inconsistent authentication, and no centralized management. Environment registration ensures compliance, versioning, and shared governance—all required if you plan to connect this into DLP later.For extra finesse, document connector capabilities in Dataverse tables so Copilot can self-describe its options. When someone asks, “What can you automate for procurement?” the AI can query those metadata entries and answer intelligently: “I can access inventory levels, purchase orders, and vendor risk data.” Congratulations, your AI now reads its own documentation.The reason this method delivers ROI isn’t mystical—it’s mechanical. Every second Copilot saves must survive transfer into workflow. Out‑of‑box connectors plateau fast. Custom Connectors punch through that ceiling by bridging the blind spots of your enterprise.Now that Copilot can see—securely and contextually—let’s make it act where people actually live: inside the apps they stare at all day.2. Adaptive Cards – Turning Suggestions into Instant ActionsCopilot’s words are smart; your users, less so when they copy‑paste them into other apps to actually do something. The typical pattern is tragicomic: Copilot summarizes a project risk, the team nods, then opens five different tools just to fix one item. That’s not automation. That’s a relay race with extra paperwork.Adaptive Cards repair that human bottleneck by planting the “Act” button directly where people already are—Teams, Outlook, or even Loop. They convert ideas into executable objects. Instead of saying “you should approve this,” Copilot can post a card that is the approval form. You press a button; Power Automate does the rest.Here’s why this matters: attention span. Every time a user switches context, they incur friction—those few seconds of mental reboot that destroy your supposed AI productivity gains. Adaptive Cards eliminate the jump. They let Copilot hand users an action inline, maintaining thread continuity and measurable velocity.So what are they, technically? Structured JSON wrapped in elegance. Each card defines containers, text blocks, inputs, and actions. Power Automate uses the “Post Adaptive Card and Wait for a Response” or the modern “Send Adaptive Card to Teams” action to push them into chat. When a recipient clicks a button—Approve, Escalate, Comment—the response event triggers your next flow stage. No tab‑hopping, no missing links, no “I’ll do it later.”Implementation sounds scarier than it is. Start inside Power Automate. Build your Copilot prompt logic—say, after Copilot drafts a meeting summary identifying overdue tasks. Add the Post Adaptive Card action. Design the card JSON: a title (“Overdue Tasks”), a descriptive text block listing items, and buttons bound to dynamic fields derived from Copilot’s output. When someone selects “Mark Complete,” it triggers another flow that updates Planner or your internal ticket system.Now, you’ve transformed a suggestion into a closed feedback loop. Copilot reads conversation context, surfaces an action card, users respond in‑place, and the workflow executes—all without leaving the chat thread. That seamlessness is what converts novelty into ROI.A proper design principle here: the card shouldn’t require explanation. If you have to post instructions next to it, you’ve failed the design review. Use icons, concise labels, and dynamic previews—Copilot can populate summaries like “Task: Update client pitch deck – Due in 2 days.” People click; Power Automate handles the rest. You measure completion time, not comprehension time.And yes, they work beyond Teams. In Outlook, Adaptive Cards appear inline in email—perfect for scenarios like approval requests, time‑off conf

    24 phút

Giới Thiệu

Welcome to the M365 Show — your essential podcast for everything Microsoft 365, Azure, and beyond. Join us as we explore the latest developments across Power BI, Power Platform, Microsoft Teams, Viva, Fabric, Purview, Security, and the entire Microsoft ecosystem. Each episode delivers expert insights, real-world use cases, best practices, and interviews with industry leaders to help you stay ahead in the fast-moving world of cloud, collaboration, and data innovation. Whether you're an IT professional, business leader, developer, or data enthusiast, the M365 Show brings the knowledge, trends, and strategies you need to thrive in the modern digital workplace. Tune in, level up, and make the most of everything Microsoft has to offer. Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.

Có Thể Bạn Cũng Thích